The state-of-the-art approach to predicting atomic forces is physics-based message-passing Graph Neural Networks (GNNs) [1]}, with the representative models being SchNet [2]} and DimeNet [3]}, [4]}. These GNNs first predict the energy of the entire system in a rotation-invariant manner, and then predict the per-atom forces by taking the derivative of the energy with respect to the atomic positions.
By the architecture's design, these GNNs produce forces that obey the basic physical rules of rotation-covariance and energy-conservation.
